Spotting differences

Adam Shen

January 6, 2026

Motivation

When refactoring, testing, or debugging code, we often want to check that our new results match the old results.
When differences exist, we may also be interested in where and how they differ.

The `{waldo}` package

# install.packages("pak")
pak::pak("waldo")

library(waldo)

Inspired by the Waldo books where one needs to find Waldo among many Waldo-lookalikes.
Finding Waldo is quite similar to when you're trying to check if your new results match the old results.

`waldo::compare()`

The main function of the {waldo} package is compare(). Differences between objects are coloured. Quickly running through the examples from the website:

Deletion

compare(c("a", "b", "c"), c("a", "b"))

`old`: "a" "b" "c"
`new`: "a" "b"

Addition

compare(c("a", "b"), c("a", "b", "c"))

`old`: "a" "b"    
`new`: "a" "b" "c"

Change

compare(c("a", "b", "c"), c("a", "B", "c"))

`old`: "a" "b" "c"
`new`: "a" "B" "c"

Comparing data frames

Let's see how it looks when we compare data frames.

library(tidyverse)

Add an `id` column to `rock` data set

Let's take the rock data set from base-R and add an id column so that we can easily update rows by row number.

rock <- datasets::rock %>%
  as_tibble() %>%
  mutate(
    across(everything(), as.double),
    id = 1:n()
  ) %>%
  relocate(id)

rock

# A tibble: 48 × 5
      id  area  peri  shape  perm
   <int> <dbl> <dbl>  <dbl> <dbl>
 1     1  4990 2792. 0.0903   6.3
 2     2  7002 3893. 0.149    6.3
 3     3  7558 3931. 0.183    6.3
 4     4  7352 3869. 0.117    6.3
 5     5  7943 3949. 0.122   17.1
 6     6  7979 4010. 0.167   17.1
 7     7  9333 4346. 0.190   17.1
 8     8  8209 4345. 0.164   17.1
 9     9  8393 3682. 0.204  119  
10    10  6425 3099. 0.162  119  
# ℹ 38 more rows

Create some changes - 1

For the first change, take the fourth row and make the area value NA:

change1 <- rock %>%
  slice(4) %>%
  mutate(area = NA)

change1

# A tibble: 1 × 5
     id area   peri shape  perm
  <int> <lgl> <dbl> <dbl> <dbl>
1     4 NA    3869. 0.117   6.3

View the changes - 1

rock1 <- rock %>%
  rows_update(change1, by = "id")

rock1

# A tibble: 48 × 5
      id  area  peri  shape  perm
   <int> <dbl> <dbl>  <dbl> <dbl>
 1     1  4990 2792. 0.0903   6.3
 2     2  7002 3893. 0.149    6.3
 3     3  7558 3931. 0.183    6.3
 4     4    NA 3869. 0.117    6.3
 5     5  7943 3949. 0.122   17.1
 6     6  7979 4010. 0.167   17.1
 7     7  9333 4346. 0.190   17.1
 8     8  8209 4345. 0.164   17.1
 9     9  8393 3682. 0.204  119  
10    10  6425 3099. 0.162  119  
# ℹ 38 more rows

Comparison - 1

compare(rock, rock1)

old vs new
             area
  old[1, ]   4990
  old[2, ]   7002
  old[3, ]   7558
- old[4, ]   7352
+ new[4, ]     NA
  old[5, ]   7943
  old[6, ]   7979
  old[7, ]   9333

`old$area[1:7]`: 4990.0 7002.0 7558.0 7352.0 7943.0 7979.0 9333.0
`new$area[1:7]`: 4990.0 7002.0 7558.0     NA 7943.0 7979.0 9333.0

Create some changes - 2

For the second change, take rows 8, 10, and 11, and multiply all values (except id) by 1.10.

change2 <- rock %>%
  slice(c(8, 10, 11)) %>%
  mutate(
    across(
      -id,
      \(x) x * 1.10
    )
  )

change2

# A tibble: 3 × 5
     id   area  peri shape  perm
  <int>  <dbl> <dbl> <dbl> <dbl>
1     8  9030. 4779. 0.181  18.8
2    10  7068. 3409. 0.179 131. 
3    11 10300. 4928. 0.166 131.

View the changes - 2

rock2 <- rock %>%
  rows_update(change2, by = "id")

rock2

# A tibble: 48 × 5
      id  area  peri  shape  perm
   <int> <dbl> <dbl>  <dbl> <dbl>
 1     1 4990  2792. 0.0903   6.3
 2     2 7002  3893. 0.149    6.3
 3     3 7558  3931. 0.183    6.3
 4     4 7352  3869. 0.117    6.3
 5     5 7943  3949. 0.122   17.1
 6     6 7979  4010. 0.167   17.1
 7     7 9333  4346. 0.190   17.1
 8     8 9030. 4779. 0.181   18.8
 9     9 8393  3682. 0.204  119  
10    10 7068. 3409. 0.179  131. 
# ℹ 38 more rows

Comparison - 2

compare(rock, rock2)

old vs new
               area     peri     shape    perm
  old[5, ]   7943.0 3948.540 0.1224170   17.10
  old[6, ]   7979.0 4010.150 0.1670450   17.10
  old[7, ]   9333.0 4345.750 0.1896510   17.10
- old[8, ]   8209.0 4344.750 0.1641270   17.10
+ new[8, ]   9029.9 4779.225 0.1805397   18.81
  old[9, ]   8393.0 3682.040 0.2036540  119.00
- old[10, ]  6425.0 3098.650 0.1623940  119.00
+ new[10, ]  7067.5 3408.515 0.1786334  130.90
- old[11, ]  9364.0 4480.050 0.1509440  119.00
+ new[11, ] 10300.4 4928.055 0.1660384  130.90
  old[12, ]  8624.0 3986.240 0.1481410  119.00
  old[13, ] 10651.0 4036.540 0.2285950   82.40
  old[14, ]  8868.0 3518.040 0.2316230   82.40

`old$area[5:14]`: 7943.0 7979.0 9333.0 8209.0 8393.0 6425.0  9364.0 8624.0 10651.0 8868.0
`new$area[5:14]`: 7943.0 7979.0 9333.0 9029.9 8393.0 7067.5 10300.4 8624.0 10651.0 8868.0

`old$peri[5:14]`: 3948.5 4010.2 4345.8 4344.8 3682.0 3098.7 4480.1 3986.2 4036.5 3518.0
`new$peri[5:14]`: 3948.5 4010.2 4345.8 4779.2 3682.0 3408.5 4928.1 3986.2 4036.5 3518.0

`old$shape[5:14]`: 0.122 0.167 0.190 0.164 0.204 0.162 0.151 0.148 0.229 0.232
`new$shape[5:14]`: 0.122 0.167 0.190 0.181 0.204 0.179 0.166 0.148 0.229 0.232

`old$perm[5:14]`: 17.1 17.1 17.1 17.1 119.0 119.0 119.0 119.0 82.4 82.4
`new$perm[5:14]`: 17.1 17.1 17.1 18.8 119.0 130.9 130.9 119.0 82.4 82.4

Summary

Use waldo::compare() to easily compare changes between objects.
Note: waldo::compare() works for vectors, data frames, lists, and more!